10 posts tagged with "computer vision"

Computer vision is transforming industries by automating decision making based on visual data. From facial recognition to autonomous driving, the need for efficient computer vision solutions is growing rapidly. This article explores how Roboflow combined with ReductStore, a time-series object store optimized for managing continuous data streams, can improve computer vision applications. ReductStore is designed to efficiently handle high-frequency time-series data, such as video streams, making it a perfect fit for storing and retrieving large datasets generated by computer vision tasks.

When it comes to computer vision, data storage is a critical component. You need to be able to store images for model training, as well as the results of the processing for model validation. There are a few ways to go about this, each with its own advantages and disadvantages. In this post, we’ll take a look at three different ways to store data in computer vision applications: a file system, an S3-like object storage and ReductStore. We’ll also discuss some of the pros and cons of each option.

A Simple Computer Vision Application​

For demonstration purposes, we’ll use a simple computer vision application which is connected to a CV camera and runs on an edge device:

The camera driver captures images from the CV camera every second and forwards them to the model, which then detects objects and displays the results in the user interface.

Your images and results need to be stored for training and validation purposes. The customer may also wish to view images featuring anomalous objects. These requirements present the challenge of maintaining a history of blob or unstructured data.

The Robot Operating System (ROS) stands as a versatile framework for developing sophisticated robotic applications with various sensors, including cameras. These cameras are relatively inexpensive and widely used as they can provide a wealth of information about the robot's environment.

Processing camera output with computer vision requires efficient solutions to handle massive amounts of data in real time. ROS 2 is designed with this in mind, but it is a communication middleware and does not provide a built-in solution for storing and managing large volumes of image data.

Addressing this challenge, this blog post will guide you through setting up ROS 2 with ReductStore—a time-series database for unstructured data optimized for edge computing, ensuring your robotic applications can process and store camera outputs effectively.